How Can Supply Chain Executives Use AI to Find Freight Savings?

Where Freight Savings Hide in a Network

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Six patterns account for most recoverable freight spend, and none of them appear in a monthly spend report.

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• Lanes priced above market: a rate that was competitive at award time sits above the current market months later, and the invoice matches the contract the entire time, so nothing flags it.
• Over-tendering to expensive carriers: volume keeps flowing to a carrier who runs consistently above the lane average while a cheaper contracted carrier on the same lane has capacity to absorb it.
• High cost deviation within a lane: when loads on the same lane vary widely in cost, the spread itself is the opportunity, and the carriers driving it are identifiable.
• Spot exposure: lanes running repeated spot loads that could be contracted, where a premium is being paid every week.
• LTL loads that should be FTL: LTL loads on a lane that cost more than that lane's FTL average, which is measurable once someone compares them.
• FTL loads that should be intermodal: long-haul truckload volume on lanes where intermodal is a viable option, where the savings only show up if someone models the conversion.

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An analyst can find any of these. The constraint is that checking a lane properly takes time, so most networks get reviewed in rotation or triaged down to the largest lanes by spend, and the rest run unexamined between bids.

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How AI Surfaces the Opportunities

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Every lane is scored against benchmarks continuously

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Each lane and carrier combination is compared against market data on an ongoing basis, not once at bid time. In GoodShip, those comparisons run against the market and against your own budget, with a toggle between linehaul and all-in.

Your budget is the number the business committed to, so a lane can sit at market and still be over plan. Both comparisons have to be there.

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Opportunities are ranked by total savings potential

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GoodShip calculates savings potential as the gap between your average actual cost on a lane with a given carrier and the benchmark, multiplied by the loads on that lane in the period. Lanes with high volume, a wide gap, or both rise to the top of the list. The Insights page shows the top opportunities per category, grouped at the lane and carrier level, so a lane appears more than once when several carriers run it. That grouping matters because the opportunity is often specific to one carrier on the lane.

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Separate views for cost, service, spot, routing guide, and mode

GoodShip splits this across five views. Savings Opportunities covers rate gaps, Service Opportunities ranks lanes by late loads, and Spot Opportunities ranks them by spot load count for conversion to contract. Routing Guide Optimization does the most work, surfacing lanes where loads deviate significantly from the lane average alongside the most frequently tendered carriers, their tender scores, and their rates against the lane average. LTL to FTL Conversion shows lanes where LTL loads cost more than the FTL average.

Each view has a dollar figure at the top, so an executive can see the size of each category before deciding where the team should spend its attention.

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Questions get answered without building a report

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Alongside the predefined dashboards, teams can query the data directly through Laney, GoodShip's AI transportation analyst. Laney answers questions against your own network data, and the question types map to what executives ask:

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• Which lanes are priced above market by more than 10%, and where are we tendering to higher-cost carriers when lower-cost contracted options exist?
• Which lanes had the biggest cost increase over the last six weeks, and what drove it?
• What is the cost impact of removing a carrier from the network, and what are that carrier's metrics and open issues ahead of the meeting?
• What would we save converting an eligible lane from FTL to intermodal, or shifting 20% of long-haul truckload volume to intermodal?
• Compare two months and separate controllable cost changes from market-driven ones.
• Where are we most exposed if spot rates rise 15%, and what happens to cost if we optimize for service only?

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Laney's recommendations account for service and operating performance such as tender acceptance and on-time delivery, and follow-up questions can test whether a cost-saving move introduces service risk.

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Turning an Opportunity Into Savings

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Renegotiate the rate directly

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From a savings opportunity, you can propose a new rate to the carrier without leaving the page. Enter a proposed all-in rate and GoodShip calculates linehaul and linehaul RPM using current fuel prices and your fuel schedule on file, with an optional comment for context.

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The carrier's main contacts receive the proposal by email and can accept or decline. Status badges on the page show which proposals are accepted, pending, or rejected, so a rate negotiation across dozens of lanes stays visible in one view. Carriers do not see market rates in GoodShip.

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Reallocate volume or adjust the routing guide

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Some opportunities are about tendering behavior. When Routing Guide Optimization shows a carrier being over-tendered at a rate well above the lane average while another contracted carrier on the same lane sits below it, the fix is moving volume.

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The same view surfaces the opposite problem, where a top partner is being under-tendered against the commitments made in the last RFP.

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Put the lane into a bid event

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When a rate is far enough off market that renegotiation will not close the gap, the lane goes into a bid event. GoodShip supports targeted mini-bids as well as full network RFPs, and after bids come in, Scenario Builder models award options under constraints like minimum on-time performance, required asset mix, and lane or network incumbency thresholds.

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When the event ends, awarded lanes convert automatically into network commitments in your routing guide.

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Assign the action and track what it returned

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This is the step that turns a savings list into a savings number. Every insight has an Action Plan attached, where you select from a default set of actions or write your own, assign it to a teammate, and set a date of action.

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GoodShip captures the lane analytics at the moment the action is taken, so the cost movement that follows is measurable against it. You can filter action plans by assignee and by date, mark them resolved, and review the history of actions alongside the recommendations that prompted them.

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For an executive, this is the difference between reporting on identified savings and reporting on realized savings, with a named owner on each item.

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How to Tell Whether Your Team Needs AI for This

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The simplest test is to ask your team four questions and time how long the answers take. If they come back in seconds, your current setup is working. If they take days, the analysis is being rebuilt by hand every time, and that is the gap AI closes.

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• Which lanes are priced above market right now, and by how much? Answering this by hand means pulling rates, joining benchmark data, and doing it lane by lane.
• Where are we tendering to a higher-cost carrier when a cheaper contracted option is available on the same lane? This requires comparing carrier rates and tender behavior within each lane, which is why it rarely gets checked.
• What did we do about the last set of savings opportunities, who owned each one, and what did the lane cost before and after? Without actions and dates attached to the analysis, no one can answer this at all.
• How much of the network does our savings analysis cover? Coverage determines whether the list reflects your whole network or the lanes someone had time to review.

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Why the Timing Matters in the Current Market

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Contract rates set in the 2026 bid season have been less durable than usual. FreightWaves reported in June 2026 that truckload contract rates set early in the season were not holding, with mini-bid activity spiking and some shippers rebidding their entire book as tender rejections surged.

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When routing guides break mid-contract, cost moves between annual reviews, which raises the value of continuous benchmarking and a fast path from a surfaced gap to a rate proposal or a targeted bid.

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What Determines Whether the Savings Land

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AI finds freight savings by scoring every lane and carrier combination against market and budget continuously, ranking opportunities by the dollars available, and connecting each one to an action a named person owns. The categories to look at are rate gaps against market, over-tendering to high-cost carriers, cost deviation within a lane, spot exposure, and mode conversion in both LTL to FTL and FTL to intermodal.

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Whether any of it reaches the budget depends on what happens after the opportunity appears, and that means renegotiation, volume reallocation, or a bid event, with the lane analytics captured at the time of the action so the result is measurable.

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